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Analysis of institutional authors

Ballestar MtCorresponding AuthorMir M.c.AuthorPedrera L.m.d.AuthorSainz J.Author

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January 8, 2024
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Effectiveness of tutoring at school: A machine learning evaluation

Publicated to:Technological Forecasting And Social Change. 199 123043- - 2024-02-01 199(), DOI: 10.1016/j.techfore.2023.123043

Authors: Ballestar, Maria Teresa; Mir, Miguel Cuerdo; Pedrera, Luis Miguel Doncel; Sainz, Jorge

Affiliations

Res Grp Study & Evaluat Econ Pol, Madrid, Spain - Author
Research Group for the Study and Evaluation of Economic Policies, Spain - Author
Teaching Innovat Grp Econ & Finance, Madrid, Spain - Author
Teaching Innovation Group in Economics and Finance, Spain - Author
Univ Rey Juan Carlos, Madrid, Spain - Author
Universidad Rey Juan Carlos, Madrid, Spain - Author
Universidad Rey Juan Carlos, Madrid, Spain, Research Group for the Study and Evaluation of Economic Policies, Spain - Author
Universidad Rey Juan Carlos, Madrid, Spain, Teaching Innovation Group in Economics and Finance, Spain - Author
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Abstract

Tutoring programs are effective in reducing school failures among at-risk students. However, there is still room for improvement in maximising the social returns they provide on investments. Many factors and components can affect student engagement in a program and academic success. This complexity presents a challenge for Public Administrations to use their budgets as efficiently as possible. Our research focuses on providing public administration with advanced decision-making tools. First, we analyse a database with information on 2066 students of the Programa para la Mejora de Éxito Educativo (Programme for the Improvement of Academic Success) of the Junta de Comunidades de Castilla y Léon in Spain, in 2018–2019, the academic year previous to the pandemic. This program is designed to help schools with students at risk of failure in Spanish, literature, mathematics, and English. We developed a machine learning model (ML) based on Kohonen self-organising maps (SOMs), which are a type of unsupervised (ANN), to group students based on their characteristics, the type of tutoring program in which they were enrolled, and their results in both the completion of the program and the 4th year of Compulsory Secondary Education (ESO). Second, we evaluated the results of tutoring programs and identified and explained how different factors and components affect student engagement and academic success. Our findings provide Public Administrations with better decision-making tools to evaluate and measure the results of tutoring programs in terms of social return on investment, improve the design of these programs, and choose the students to enrol. © 2023 The Author(s)

Keywords

artificial neural networkselementaryidentificationinterventionspatternspublic policy analysisstudentsteacherstutoring programArtificial neural networksBudget controlComputer simulationDecision makingDecision making toolLearning evaluationsLearning systemsMachine learningMachine learning modelsMachine-learningNumerical modelPolicy analysisPublic administrationPublic policy analysePublic policy analysisResearch focusRisk of failureSelf-organizing mapsStudent engagementStudentsTechnological changeTechnological developmentTutoring program

Quality index

Bibliometric impact. Analysis of the contribution and dissemination channel

The work has been published in the journal Technological Forecasting And Social Change due to its progression and the good impact it has achieved in recent years, according to the agency WoS (JCR), it has become a reference in its field. In the year of publication of the work, 2024 there are still no calculated indicators, but in 2023, it was in position 1/54, thus managing to position itself as a Q1 (Primer Cuartil), in the category REGIONAL & URBAN PLANNING. Notably, the journal is positioned above the 90th percentile.

Independientemente del impacto esperado determinado por el canal de difusión, es importante destacar el impacto real observado de la propia aportación.

Según las diferentes agencias de indexación, el número de citas acumuladas por esta publicación hasta la fecha 2025-07-17:

  • WoS: 1
  • Scopus: 1

Impact and social visibility

From the perspective of influence or social adoption, and based on metrics associated with mentions and interactions provided by agencies specializing in calculating the so-called "Alternative or Social Metrics," we can highlight as of 2025-07-17:

  • The use, from an academic perspective evidenced by the Altmetric agency indicator referring to aggregations made by the personal bibliographic manager Mendeley, gives us a total of: 74.
  • The use of this contribution in bookmarks, code forks, additions to favorite lists for recurrent reading, as well as general views, indicates that someone is using the publication as a basis for their current work. This may be a notable indicator of future more formal and academic citations. This claim is supported by the result of the "Capture" indicator, which yields a total of: 92 (PlumX).

With a more dissemination-oriented intent and targeting more general audiences, we can observe other more global scores such as:

  • The Total Score from Altmetric: 62.55.
  • The number of mentions on the social network X (formerly Twitter): 81 (Altmetric).

Leadership analysis of institutional authors

There is a significant leadership presence as some of the institution’s authors appear as the first or last signer, detailed as follows: First Author (Ballestar de las Heras, María Teresa) and Last Author (Sainz González, Jorge).

the author responsible for correspondence tasks has been Ballestar de las Heras, María Teresa.